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Machine learning-based prediction models for parathyroid carcinoma using pre-surgery cognitive function and clinical

Yuting Wang¹, Bojun Wei², Teng Zhao¹

¹Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.

Scientific Reports

|November 4, 2023

Summary

This summary is machine-generated.

Preoperative cognitive function, assessed by MMSE and MOCA scores, can predict parathyroid carcinoma (PC). Machine learning models, particularly XGBoost, show promise in identifying PC before surgery, improving patient outcomes.

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Area of Science:

Endocrinology
Oncology
Medical Informatics

Background:

Parathyroid carcinoma (PC) diagnosis is often delayed until after surgery, leading to poor patient outcomes.
Early detection of PC is crucial for improving surgical success and patient prognosis.

Purpose of the Study:

To identify pre-surgery indicators of parathyroid carcinoma (PC).
To develop a machine learning-based predictive model for PC using preoperative data.

Main Methods:

Evaluated 133 primary hyperparathyroidism patients, assessing pre-surgery neuropsychological function and pathology.
Utilized machine learning algorithms, including extreme gradient boosting (XGBoost) and LASSO regression, for model development.
Compared machine learning models against logistic regression for predictive accuracy.

Main Results:

Elevated parathyroid hormone and decreased serum phosphorus were significant indicators in PC patients.
Lower scores on the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MOCA) were observed in PC patients.
The XGBoost model achieved a higher Area Under the Curve (AUC) of 0.835 compared to logistic regression (0.683) and LASSO (0.607).

Conclusions:

Preoperative cognitive function, specifically MMSE and MOCA scores, may serve as a predictor for PC.
A cognitive function-based prediction model using XGBoost outperformed traditional methods, offering valuable preoperative decision-making support for suspected PC cases.